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Joint Models for Incomplete Longitudinal Data and Time-to-Event Data

Author

Listed:
  • Yuriko Takeda

    (Graduate School of Medicine, Yokohama City University, Yokohama 236-0004, Japan)

  • Toshihiro Misumi

    (Department of Biostatistics, Yokohama City University School of Medicine, Yokohama 236-0004, Japan)

  • Kouji Yamamoto

    (Department of Biostatistics, Yokohama City University School of Medicine, Yokohama 236-0004, Japan)

Abstract

Clinical studies often collect longitudinal and time-to-event data for each subject. Joint modeling is a powerful methodology for evaluating the association between these data. The existing models, however, have not sufficiently addressed the problem of missing data, which are commonly encountered in longitudinal studies. In this paper, we introduce a novel joint model with shared random effects for incomplete longitudinal data and time-to-event data. Our proposed joint model consists of three submodels: a linear mixed model for the longitudinal data, a Cox proportional hazard model for the time-to-event data, and a Cox proportional hazard model for the time-to-dropout from the study. By simultaneously estimating the parameters included in these submodels, the biases of estimators are expected to decrease under two missing scenarios. We estimated the proposed model by Bayesian approach, and the performance of our method was evaluated through Monte Carlo simulation studies.

Suggested Citation

  • Yuriko Takeda & Toshihiro Misumi & Kouji Yamamoto, 2022. "Joint Models for Incomplete Longitudinal Data and Time-to-Event Data," Mathematics, MDPI, vol. 10(19), pages 1-7, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3656-:d:934403
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    References listed on IDEAS

    as
    1. Geert Molenberghs & Caroline Beunckens & Cristina Sotto & Michael G. Kenward, 2008. "Every missingness not at random model has a missingness at random counterpart with equal fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(2), pages 371-388, April.
    2. Christos Thomadakis & Loukia Meligkotsidou & Nikos Pantazis & Giota Touloumi, 2019. "Longitudinal and time‐to‐drop‐out joint models can lead to seriously biased estimates when the drop‐out mechanism is at random," Biometrics, The International Biometric Society, vol. 75(1), pages 58-68, March.
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